code: def test_timeseries_input_feature(
      timeseries_config: Dict, encoder: str
  ) -> None:
      timeseries_config[ENCODER][TYPE] = encoder
  
      timeseries_config, _ = load_config_with_kwargs(
          TimeseriesInputFeatureConfig, timeseries_config
      )
      timeseries_input_feature = TimeseriesInputFeature(
          timeseries_config
      ).to(DEVICE)
      timeseries_tensor = torch.randn(
          [SEQ_SIZE, TIMESERIES_W_SIZE], dtype=torch.float32
      ).to(DEVICE)
      encoder_output = timeseries_input_feature(timeseries_tensor)
      assert (
          encoder_output[ENCODER_OUTPUT].shape[1:]
          == timeseries_input_feature.output_shape
      )
  
file_path: ludwig/tests/ludwig/features/test_timeseries_feature.py
imports: from typing import Dict
  import pytest
  import torch
  from ludwig.constants import ENCODER, ENCODER_OUTPUT, TYPE
  from ludwig.features.timeseries_feature import TimeseriesInputFeature
  from ludwig.schema.features.timeseries_feature import TimeseriesInputFeatureConfig
  from ludwig.schema.utils import load_config_with_kwargs
  from ludwig.utils.torch_utils import get_torch_device
label_desc: Use image node_id 2 for calling a global function with example usage: test_timeseries_input_feature(timeseries_config, encoder) without return types